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You searched for: creator:"Oleg Michailovich"
[texts]An Iterative Shrinkage Approach to Total-Variation Image Restoration - Oleg Michailovich
The problem of restoration of digital images from their degraded measurements plays a central role in a multitude of practically important applications. A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modeled by an ill-conditioned operator. In such a case, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some a priori information about its properties...
Downloads: 2
[texts]Regularized Richardson-Lucy Algorithm for Sparse Reconstruction of Poissonian Images - Elad Shaked
Restoration of digital images from their degraded measurements has always been a problem of great theoretical and practical importance in numerous applications of imaging sciences. A specific solution to the problem of image restoration is generally determined by the nature of degradation phenomenon as well as by the statistical properties of measurement noises. The present study is concerned with the case in which the images of interest are corrupted by convolutional blurs and Poisson noises...
Downloads: 8
[texts]Image Segmentation Using Weak Shape Priors - Robert Sheng Xu
The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of strong noise. To overcome this problem, the present paper introduces a novel approach segmentation through the use of "weak" shape priors. Specifically, in the proposed method, an segmenting active contour is constrained to converge to a configuration at which its geometric parameters attain their empirical probabilit...
Downloads: 3
[texts]Spatially regularized compressed sensing of diffusion MRI data - Oleg Michailovich
The present paper introduces a method for substantial reduction of the number of diffusion encoding gradients required for reliable reconstruction of HARDI signals. The method exploits the theory of compressed sensing (CS), which establishes conditions on which a signal of interest can be recovered from its under-sampled measurements, provided that the signal admits a sparse representation in the domain of a linear transform...
Downloads: 1
[texts]Image Deblurring Using Derivative Compressed Sensing for Optical Imaging Application - Mohammad Rostami
Reconstruction of multidimensional signals from the samples of their partial derivatives is known to be a standard problem in inverse theory. Such and similar problems routinely arise in numerous areas of applied sciences, including optical imaging, laser interferometry, computer vision, remote sensing and control. Though being ill-posed in nature, the above problem can be solved in a unique and stable manner, provided proper regularization and relevant boundary conditions...
Downloads: 130
[texts]Information tracking approach to segmentation of ultrasound imagery of prostate - Robert Sheng Xu
The size and geometry of the prostate are known to be pivotal quantities used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the above-mentioned quantities are based on using imagery data of prostate. The necessity to process large volumes of such data creates a need for automatic segmentation tools which would allow the estimation to be carried out with maximum accuracy and ef...
Downloads: 6
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